Can Granite Code 20B run on Intel Arc Pro B60 24GB?

YES — Runs Great

A82Great

Estimated from fit model

Granite Code 20B needs ~18.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 18.7 GB, 21.8 tok/s, Runs well

18.7 GB required24.0 GB available

78% VRAM used

Fit status

Runs well

Decode

21.8 tok/s

TTFT

8881 ms

Safe context

Memory

18.7 GB / 24.0 GB

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite Code 20B on Intel Arc Pro B60 24GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 21.8 tok/s decode · 8.9s TTFT (warm) · 55 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	21.8 tok/s	4844 ms	8K
Coding	A	Runs well	21.8 tok/s	8881 ms	8K
Agentic Coding	A	Tight fit	21.8 tok/s	12918 ms	8K
Reasoning	A	Runs well	21.8 tok/s	10496 ms	8K
RAG	A	Tight fit	21.8 tok/s	16148 ms	8K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	A77
Q3_K_S	3	9.8 GB	Low	A78
NVFP4	4	11.2 GB	Medium	A79
Q4_K_M	4	12.2 GB	Medium	A80
Q5_K_M	5	14.4 GB	High	A80
Q6_KBest for your GPU	6	16.4 GB	High	A79
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Your hardware

More models your Intel Arc Pro B60 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s
Qwen 3.6 27B	27B	S	12.3 tok/s
Qwen 3.6 35B A3B	35B	A	16.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	38.5 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Granite Code 20B?

Yes, Intel Arc Pro B60 24GB can run Granite Code 20B with a A grade (Runs well). Expected decode speed: 21.8 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 18.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Granite Code 20B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8881ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on Intel Arc Pro B60 24GB receives a A grade with 21.8 tok/s and 8K context.

What context window can Granite Code 20B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Granite Code 20B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on Intel Arc Pro B60 24GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Granite Code 20B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc Pro B60 24GB See all hardware for Granite Code 20B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/granite-code-20b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: